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GPGPU programming

GPU data processing in luma.gl ranges from portable lazy vector operations to higher-level experimental analytical modules. Start with the highest-level API that expresses the workflow cleanly.

Outcome and prerequisites​

This guide helps you choose between the stable @luma.gl/gpgpu evaluators, its experimental subpaths, Engine compute helpers, and experimental GPU tables. It assumes familiarity with buffers and typed arrays; the Core GPU guide introduces those concepts.

Mental model​

NeedStart with
Portable lazy vector expressions on CPU, WebGL 2, or WebGPU@luma.gl/gpgpu
Primitive GPU chunks, vectors, and memory formats@luma.gl/gpgpu/gpu-data
Batch-preserving GPU tables and schemas@luma.gl/experimental/gpu-tables
One explicit buffer or texture computationEngine compute helpers

Complete workflow​

import {GPUDataEvaluator, add, cleanEvaluate} from '@luma.gl/gpgpu';

const values = GPUDataEvaluator.fromArray(new Float32Array([1, 2, 3]), {size: 1});
const adjusted = add(values, GPUDataEvaluator.fromConstant(0.5));
const result = await cleanEvaluate(device, adjusted);

Operations stay lazy until evaluation. cleanEvaluate() retains the requested output and releases intermediate evaluator results.

Decisions and tradeoffs​

  • Prefer GPGPU evaluators when portability and expression composition matter most.
  • Preserve source chunks in GPU vectors and tables instead of silently repacking streaming data.
  • Avoid readback between operations unless the CPU actually needs the result.

Common mistakes​

  • Evaluating every intermediate expression separately instead of evaluating the final expression.
  • Treating GPU memory layout and shader value types as the same contract.

Next steps​